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Risk Assessment – Garm (TJ)
FOUNDATIONS FOR RISK ASSESSMENT & MANAGEMENT PROJECT GROUP 4
2015/10/12
ERJAUTZ, JULIAN (19910410T432) GARRELS, LANA (19911209T385) MARTEY, MICHELLE (19891026T349) RÜRUP, ANNA (19910323T305) DO, ANH (19930120P225)
i
Executive Summary The city of Garm, due to its geographical location and features, is subjected to a number of different
natural hazards. Three major risks to the people of Garm are identified to be: earthquakes, mudflows and
floods. Focusing on the main objective of analysing possible fatalities, these risk assessment quantifies the
magnitude of the three risks. Using the general framework as outlined in Tehler, 2015, this report
elaborates on the first five steps of risk assessment for the city of Garm: identify value/objective (fatalities),
describe the context/background, identify risk scenarios, analyse consequences and likelihoods and finally,
risk presentation. Details of the methodology, risk scenarios and their consequences and likelihoods,
calculations of societal and individuals risks as well as a sensitivity analysis are presented in the report.
The risk assessment carries out a number of calculations to determine the magnitude of the risks. To assist
understanding of the results, some key figures are included in the report. A map outlining the structural
model of the city is constructed to identify risk areas. Event trees for each of the risks are also presented
to identify risk scenarios and calculate their likelihood. As crucial content for the risk presentation, two
diagrams of Frequency-Number (FN) curves summarizes visually the consequences and probability of
those scenarios. Finally, a tornado diagram in the sensitivity analysis provides information about the
uncertainties in the risk assessment.
In summary, this assessment finds that the expected fatalities per year due to earthquakes is 230, floods
is 7 and mudflows is 7. However, these numbers are not the concluding statement. The uncertainties in
the model, completeness and parameters are very important in the justification of obtaining the expected
fatalities results. It is found that uncertainties in the frequency of occurrence of a small earthquake and
the possibility that an adobe building will collapse in a small earthquakes are two parameters that affect
the result of the assessment the most. The discussion chapter of the report provides insight to how these
numbers can be interpreted to estimate the level of hazards affecting Garm. Bringing together the
assumptions, limitations and uncertainties discussed throughout the report, the conclusion highlights
social and economic aspects that lead to different risk distributions in the city. In addition, based on this
rough risk assessment, suggestions for a more detailed analysis are presented.
With the quantification of 3 natural hazards in Garm, further decisions on risk reducing measure are made
possible – step 6 of the risk assessment framework. This is however not the scope of this report.
ii
Table of Contents
Executive Summary
1. Introduction ........................................................................................................................................... 1
1.1. Background & Purpose .................................................................................................................. 1
1.2. Content .......................................................................................................................................... 1
2. Methodology ......................................................................................................................................... 2
2.1. Procedure ...................................................................................................................................... 2
2.2. Assumptions .................................................................................................................................. 3
2.3. Limitations ..................................................................................................................................... 4
3. Scenarios and Likelihood ....................................................................................................................... 4
3.1. Structural Model............................................................................................................................ 4
3.2. Event Trees .................................................................................................................................... 5
3.2.1. Event Tree for Earthquakes ................................................................................................... 5
3.2.2. Event Tree for Floods ............................................................................................................ 5
3.2.3. Event tree for mudflows ........................................................................................................ 6
3.3. Table of Risk Scenarios .................................................................................................................. 6
4. Societal Risk ........................................................................................................................................... 7
4.1. Earthquakes ................................................................................................................................... 7
4.2. Floods ............................................................................................................................................ 7
4.3. Mudflows ....................................................................................................................................... 8
4.4. FN-Curves ...................................................................................................................................... 9
5. Individual Risk ...................................................................................................................................... 10
6. Sensitivity Analysis .............................................................................................................................. 11
7. Discussion and Conclusion .................................................................................................................. 14
8. Reference List ...................................................................................................................................... 16
Appendixes
Appendix 1 – Excel Sheet for Data and Calculations ................................................................................. A
Appendix 2 – Information on Construction Types in Garm ....................................................................... B
Appendix 3 – Hazard Frequencies ............................................................................................................. C
Appendix 4 – Large Version Structural Model ........................................................................................... C
Appendix 5 – Table of Scenario Summaries .............................................................................................. D
Appendix 6 – Individual Risks plotted on Map of Garm ............................................................................ E
Appendix 7 – Geographical Area Measurements in Google Earth Screenshots ........................................ F
iii
Table of Figures Figure 1 Heat map of earthquakes since 1973 near Tajikistan ..................................................................... 1
Figure 2 – General framework for risk assessment ...................................................................................... 2
Figure 3 - Structural model for the risk assessment in form of a map .......................................................... 4
Figure 4 – Event tree earthquakes ................................................................................................................ 5
Figure 5 – Event tree floods .......................................................................................................................... 5
Figure 6 – Event tree mudflows .................................................................................................................... 6
Figure 7 – Frequency-Fatalities-Curve of individual hazards in Garm ........................................................... 9
Figure 8 - Frequency-Fatalities-Curve of all hazards in Garm combined ...................................................... 9
Figure 9 – Individual Risks plotted on map of Garm ................................................................................... 10
Figure 10 – Tornado diagram displaying sensitivity analysis results ........................................................... 13
Appendix figure 1 - The probability that a building collapses, given a certain earthquake risk scenario ..... B
Appendix figure 2 – Population in different construction types ................................................................... B
Appendix figure 3 – Structural Model Map Garm ......................................................................................... C
Appendix figure 4 - IR plotted on Map of Garm ........................................................................................... E
Table of Tables
Table 1 – Assumptions made in the risk assessment process ....................................................................... 3
Table 2 – Example calculations of IR ........................................................................................................... 10
Table 3- Sensitivity Analysis Parameter & Variation ................................................................................... 12
Appendix table 1 – Estimated hazards frequencies ...................................................................................... C
1
1. Introduction
1.1. Background & Purpose Tajikistan is a mountainous, landlocked country, with a stagnant economy and is considered one of the
poorest country in Central Asia (United Nations [UN], 2014). This risk assessment concerns the city of Garm
(or Gharm) in the Rhast Valley, next to the river Vakhsh. Although the river provides many economic
benefits, such as hydroelectric power and irrigation, it also poses a number of geological risks to the city.
Unusually high rainfall can cause rising water level in the river leading to floods that affect the population
area close to the river banks. Rain water can also saturate the ground and cause mudflows in areas further
away from the river at a higher elevation. In addition, the city is considerably close to an area of high
frequency of earthquakes as illustrated in the map in figure 1 (United Nations [UN], 2014).
The geological position and
economic situation may create
difficulties for evacuation and
rescue efforts, therefore an in-
depth understanding of the
risks can be crucial to ensuring
the safety of people in this
region.
1.2. Content Fulfilling this purpose, this risk
assessment follows the
general framework as outlined by
Henrik Tehler (2015) which will be
further introduced in chapter two, the methodology. The methodology also contains the motivations for
the assumptions that have been made. The values guiding the analysis are related to the prevention of
fatalities, which is the primary concern. Following the methodology, the context of the City of Garm as
described above is further elaborated on as a model, illustrated by a map showing the different threatened
zones at different geographical location. Based on this, the different risk scenarios are described. A
summary of the main risks that this assessment focuses on include:
Earthquakes: two analysed categories are small earthquakes, which have a magnitude of 6 or less
on the Richter scale and large earthquakes with a magnitude higher than 6 (UPSeis, n.d.). In case
of an earthquake, the whole city is exposed to the same peak acceleration and the type of
construction determines whether a house collapses. Please see appendix 2 for the expert
information provided on these building types. With the current level of rescue capacities, the
likelihood of dying in a collapsed house is 90% (Garm Case, n.d.).
Floods: according to the Garm Case (n.d.), a small flood is when the water level rises 1.5m above
normal water levels, and a large flood is when the water level is 3m above normal. The estimated
mortality rate is 2%.
Figure 1 Heat map of earthquakes since 1973 near Tajikistan with red indicating the most severe areas (maptd, 2011)
2
Mudflows: a large mudslide is 250m wide and a small one is 150m wide. Mudflows flowing towards
the city destroy everything in its way. Therefore, the same mortality rate as for earthquake
collapsed buildings is chose, 90%. Further reasoning for this assumption is provided in the
methodology on page 6.
The estimated frequencies for these events are presented in appendix 3 and the values are expert
judgements on the frequency of occurrence of all three analysed hazard types.
Subsequently, the analysis quantifies the individual and societal risk of death of each of the above risks
caused by natural disasters with calculations involving the likelihoods and consequences of different risk
scenarios. The results of these calculations are then presented in a map of individual risks and different
FN-curves. Finally, a sensitivity analysis is conducted to examine the robustness of the analysis and
therefore provide a conclusion of the risk analysis. By quantifying these risks, an understanding of the
magnitude of such disasters for the city of Garm are gained and enable further risk reducing decisions.
2. Methodology
2.1. Procedure This chapter describes the project’s methodological framework. The overall aim of the analysis is to
answer the following three questions associated with risk assessment (Tehler, 2015, p.5):
• What can happen?
• How likely is it?
• What will the consequences be?
In order to successfully answer the
questions, the project is based on
the data provided by the case study
and the “General Framework for
Risk Assessment” by H. Tehler (see
figure 2), which was used to guide
the analysis in a structured manner
by working through the steps from
the value setting to the eventual
risk evaluation. The latter is not a
part of this project, but will be
addressed in the individual
assignments succeeding this
project.
The values of the risk assessments
(step one) were set by case study: the hazards were assessed with a focus on possible fatalities. Thus, the
threat for human life guided the analysis. Moreover, step two, the general context and system description
Figure 2 – General framework for risk assessment (Tehler, 2015)
3
were mostly given by the case as well, and a corresponding google earth map was available, which served
as a structural model (chapter 3). Based on this context information, step three was carried out by the
constructions of scenario trees for all identified hazards (chapter 3). The frequencies and probabilities in
the trees (step three and four combined) were calculated from the given data based on the assumptions
presented below. Consequences as in step four and risk presentation in step five have been separated into
two focus areas: the societal risk and the individual risk for the people of Garm. The societal risk is
presented in form of FN-curves and expected consequences, which are according to the value set, the
expected fatalities. The individual risk were plotted on a map of Garm, which served as a structural model
previously. In order to enable the consequence calculations, the geographical sizes of Garm’s population
areas, threatened areas, and exposed areas were necessary, which were obtained by measurements from
Google Earth with the polygon ruler function. Eventually, the robustness of certain parameters were tested
through a sensitivity analysis and the variations were presented in a tornado diagram. Although not being
an explicit step of Tehler’s risk assessment framework, the sensitivity analysis assists the process of
choosing appropriate risk measures, as more knowledge about uncertain aspects is given. All necessary
calculations for the steps elaborated above were made in an excel sheet (see appendix 1). The necessary
assumptions to enable the risk assessment were carefully discussed and decided upon. Additional research
and referencing was conducted with guidance of the American Psychological Association’s referencing
style (American Psychological Association [APA], 2015).
2.2. Assumptions Certain assumptions were necessary for the assessment, as not all data was provided. These assumptions
as well as the factors provided by experts, for example the frequency intervals, were subject to the
sensitivity analysis to gain knowledge about the uncertainty related to these factors. The following
assumptions have been made:
Table 1 – Assumptions made in the risk assessment process
1 The expert-judged data provided in the case is reliable.
2 The average values of the expert data are adequate to obtain results that are as reliable as possible, but a sensitivity analysis is needed.
3 The measurements in google earth with the polygon ruler function contain the necessary level of detail. More detailed measurements would not have a significant impact on the consequences.
4 In order to obtain the exposed mudflow area measurements, simplified area length and width values were used, as displayed in appendix 7 on page vi. This was necessary in order to efficiently obtain the required measurements.
5 The analysed hazards are primary hazards and are not affected by a decrease in the rescue capacities due to being a secondary hazard. For example, the mudflows are assumed not to be a secondary hazard of an earthquake.
6 Therefore, the assumed mortality rate of mudflows is 90%, which is similar to the expert-judged mortality rate for earthquakes. The 10% of the affected population that can be rescued when buildings are destroyed (both assumed for earthquakes and mudflows) due to available resources might be less in case of one hazard being a secondary hazard initiated by the first one, as capacities are already used.
4
7 It is also assumed that the data separation in day and night means that 50% of the 24 hour day is night and the other half is day. This is assumed for 365 days of a year, making no difference between weekends, holidays or special occasions. The impact that another day-night ration would have on the valued fatalities, is tested in the sensitivity analysis.
2.3. Limitations
In addition to the fact that all uncertainties and assumptions are naturally limitations of the project
outcome, some other constraints need to be considered. The three analysed hazards are only a choice of
risks for the city of Garm, as accidents, diseases or highly unlikely phenomena such as asteroids can also
be risks. Furthermore, the values could have been broadened by looking at health threats and economic
damage as well, but this would exceed the scope of the project. Moreover, the sensitivity analysis could
have been done with all factors that are possible to change and for all hazard types, but it was decided to
keep the analysis efficient and within the scope of the project. Finally, the assessment team did not have
any background information on the experts and therefore no certainty exists about how the experts
arrived at the provided numbers.
3. Scenarios and Likelihood This chapter focusses on the description of the specific scenarios and the corresponding probabilities of
their occurrence. In order to develop adequate the scenarios, the context and the situation need to be
sufficiently clarified. The context description in the introduction of this report is therefore illustrated by
the following map, which serves as a structural model for the assessment.
3.1. Structural Model The map in Figure 3 displays
the different population and
hazard affected areas of the
city of Garm as well as the
risk frontiers of floods and
mudflows. Derived from this
context, three scenario trees
for each hazard type were
developed. A larger version
of the model and its detailed
description, also showing the
various population areas is
included in appendix 4.
Figure 3 - Structural model for the risk assessment in form of a map
5
3.2. Event Trees
As mentioned above, the analysis focusses on earthquakes, floods and mudflows. To appropriately display
the different scenarios and their frequency, the event tree method was chosen, which enables a
representation of the scenarios in a logical order and calculate the frequencies as well as probabilities.
The event tree for earthquakes and floods look similar in structure and contain four scenarios whereas the
mudflow tree includes twelve scenarios due to the different mudflow areas with different probabilities.
An indicating event, which differs from the optimal case S0, was taken and placed as a starting point. The
average frequency of the hazards was based on expert’s opinions, as explained above. Following this, each
event was divided into small and big events with their individual probability, followed by a division in day
and night with a probability of 0.5. The following calculation exemplifies the method used for each branch
of the scenario trees.
As an Example the calculation of Scenario 1: Earthquake F0.0825 * Small P0.909 * Day P 0.5 = S1 0.0375 = P Small earthquake during the day
3.2.1. Event Tree for Earthquakes
Figure 4 – Event tree earthquakes
3.2.2. Event Tree for Floods
Figure 5 – Event tree floods
Earthquake
F=0.0825/year
Earthquake
F=0.0825/year
Small
P=0.909
Small
P=0.909
day
P=0.5
day
P=0.5S1 = 0.0375/year
S2=0.0375/yearnight
P=0.5
night
P=0.5
S3=0.0038/year
S4=0.0038/year
Large
P=0.091
Large
P=0.091
day
P=0.5
day
P=0.5
night
P=0.5
night
P=0.5
Flood
F=0.305/year
Flood
F=0.305/year
Small
P=0.902
Small
P=0.902
day
P=0.5
day
P=0.5S5 = 0.1375/year
S6=0.1375/yearnight
P=0.5
night
P=0.5
S7=0.015/year
S8=0.015/year
Large
P=0.098
Large
P=0.098
day
P=0.5
day
P=0.5
night
P=0.5
night
P=0.5
6
3.2.3. Event tree for mudflows
Figure 6 – Event tree mudflows
3.3. Table of Risk Scenarios
The table of Risk Scenarios in appendix 5 summarizes the results of the twenty risk scenarios displayed in
the trees above as well as the consequences, covered by the societal risk section (Chapter 4). The scenarios
are further evaluated in the following sections and the table serves as an overview of all essential factors
needed. The table shows the different scenarios, the areas which are affected by the hazards and the
estimated frequency of occurrence as well as the estimated number of fatalities.
Mudflow
F=0.118/year
Mudflow
F=0.118/year
Area 1
P=0.36
Area 1
P=0.36
small
P=0.71
small
P=0.71
day
P=0.5
day
P=0.5S9 = 0.015/year
night
P=0.5
night
P=0.5S10=0.015/year
large
P=0.29
large
P=0.29
day
P=0.5
day
P=0.5S11=0.00625/year
night
P=0.5
night
P=0.5S12=0.00625/year
Area 2
P=0.38
Area 2
P=0.38
small
P=0.67
small
P=0.67
day
P=0.5
day
P=0.5S13=0.015/year
night
P=0.5
night
P=0.5S14=0.015/year
large
P=0.33
large
P=0.33
day
P=0.5
day
P=0.5S15=0.0075/year
night
P=0.5
night
P=0.5S16=0.0075/year
Area 3
P=0.26
Area 3
P=0.26
small
P=0.82
small
P=0.82
day
P=0.5
day
P=0.5S17=0.0125/year
night
P=0.5
night
P=0.5S18=0.0125/year
large
P=0.18
large
P=0.18
day
P=0.5
day
P=0.5S19=0.00275/year
night
P=0.5
night
P=0.5S20=0.00275/year
7
4. Societal Risk Societal risk is a measure of the number of people who might be affected by a hazard/risk (CCPS, 2000). It
can be expressed in terms of the frequency distribution of the hazard (F) and number of fatalities (N),
which can be combined to the form of an FN (frequency-number) curve as well as in terms of expected
consequences. The FN-curve is a plot of the cumulative frequency against consequences (number of
fatalities). Refer for all specific calculations to the excel sheet in appendix 1.
4.1. Earthquakes As explained in the context introduction, expert knowledge of earthquakes in Garm indicates that different
buildings have different probabilities of collapsing in the event of either a small or large earthquake and
90% of the people trapped in the collapsed building will die (see appendix 2 for further information).
This information is used to determine the fatalities from earthquakes in the different buildings, in different
population areas. For example: 50% of the Adobe buildings will collapse if a small earthquake occurs. Once
total number of people who are in Adobe buildings at night is established, the total number of fatalities
can be calculated as follows (refer to appendix 1 and 2 for the data).
Formula: Number of fatalities (N): N = Ae * D * M Ae = building types (all population in the type)
N(fatalities) Adobe building, small earthquake, night = 0.9*(0.5*6010) = 2704.5 People
This is calculated for all buildings in all scenarios (see appendix 1).
The expected consequence E(X), is the total number of expected fatalities of all risk scenarios. It is the sum
of the product of the each scenario’s frequency with the total number of fatalities in the particular
scenario.
Total Fatalities for all buildings in Scenario 2 (Small earthquake, night)= 3438
Frequency (F) - (as in event tree) = 0.0375
This means the expected consequences for all earthquake scenarios are: E(X) Earthquakes= 229.8797.
4.2. Floods The determination of the number of fatalities as a result of floods is slightly different from earthquakes.
Unlike earthquakes, which are experienced in the entire area, floods are restricted to specific “threatened”
areas, such as along rivers or flood plains.
According to the provided map data, the population areas exposed to flood risks are area 1, 2, 6,7,8,9 and
10. Furthermore, the severity of the risk is determined by the size of the flood and the 2% mortality rate.
As explained in the methodology, the area measurement were conducted in google earth and the
measurements can be found in appendix 7. These measurements were then used to determine the
population density of the areas affected by flooding. As an example, see the following calculation of flood
risk for large flood, during the day in population area 2:
Population density= Total population/ threatened area
= 1200/0.92km2= 1304 people/km2
8
Number of people in exposed area = 1304* 0.22 (size of area exposed to large flood) = 286.956
N = D * M because Ae = At
Area 2 fatalities (large flood, day) = 0.02* 286.956 = 5.73
This calculation is replicated for all population areas and all flood risk scenarios, eventually computing the
total fatalities.
E(X) = ∑ f(Scenario)* N( total fatalities in each scenario)
E(X) Flood = 6.8579
4.3. Mudflows The process of determining the societal risk that arises from mudflows is the same as that of floods as
highlighted above. However, with the assumption that there are 90% mortality rate in the exposed areas,
which are, contrary to the floods, unequal to the threatened area. Therefore, the whole scenario
consequence calculation is comparable to floods besides that formula for fatalities, which is for the
mudflows:
N = Ae * D * M because Ae ≠ At
Thus, the exposed and threatened areas were measured and calculated (please see appendix 1) and based
on the scenario consequences, the following expected consequences were determined.
The E(X) = ∑ f(Scenario)* N( total fatalities in each scenario)
E(X) Mudflow = 6.833
From the sections above, it can be concluded that the expected consequences for earthquake risks is on
average 230 deaths per year, while floods and mudflows have an expected consequence of 7 deaths
respectively.
9
4.4. FN-Curves Based on the findings from the calculations above, the data can be plotted into FN curves as shown below.
See appendix 1 for the individual curves.
Figure 7 – Frequency-Fatalities-Curve of individual hazards in Garm (see appendix 1)
Reflecting upon the FN-curves, higher fatalities arise from earthquakes in Garm compared to floods or
mudflows, which is due to the fact that earthquakes affect a larger geographical area as compared to the
other two hazards. However, the frequency of the earthquakes is considerably much less compared to the
mudflows and floods respectively. Combining all three hazards, the following FN-curve can be developed.
The FN Curves can later be used to determine the acceptability of the risk, such as basing on the ALARP
concept.
Figure 8 - Frequency-Fatalities-Curve of all hazards in Garm combined (see appendix 1)
0
0,05
0,1
0,15
0,2
0,25
0,3
0,35
1 10 100 1000 10000
Fre
qu
en
cy
No of Fatalities
FN-Curves Hazards Garm
Earthquakes
Floods
Mudflows
0,001
0,101
0,201
0,301
0,401
0,501
0,601
1 10 100 1000 10000
Fre
qu
en
cy
No of Fatalities
FN-Curve Combined Hazards
10
5. Individual Risk The map shown in figure 9 displays the different location-specific individual risks of dying (IR) that were
identified for the flood and mudflow threatened areas of the city of Garm, when using the average values
for frequencies. Irrespective of the population area, the IR for a small flood is 0.0061 while the IR for a
large flood is 0.0006 (flood IR = yellow). Both are determined by the formula IR = f (scenario) * M, as Ae =
At. Please see appendix 6 for a larger version of the map.
Figure 9 – Individual Risks plotted on map of Garm
However, the mudflow threatened areas (red/purple) vary per population area in the location-specific IR,
which is due to the fact that the possibly exposed areas are not equal to the threatened areas. The
corresponding formula is IR = f (scenario) * M * (Ae/ At), as Ae ≠ At. Please see the excel sheets 1 for
further information on the calculations for IR of floods and mudflows as well as the example calculations
below.
Table 2 – Example calculations of IR
Hazard Formula Calculation
Mudflow large Area 1 (population area 3)
F(large mudflow)*M*(Ae / At) 0.03*0.9*(0.3506/1.08) = 0.0088
Mudflow small Area 1 (population area 3)
F(small mudflow)*M*(Ae / At) 0.013*0.9*(0.0273/0.35) + 0.03*0.9*(0.3506/1.08) = 0.0096
11
Overall, it can be concluded that the IR is higher in the areas threatened by both small as well as large
floods/ mudflows as the frequency of small and large hazards are accumulated. Furthermore, it is
identified that the centre of Garm is least dangerous for individuals, the danger of earthquakes being
excluded. The individual risk of earthquakes is not plotted on the map, as the whole city is exposed to an
earthquake in case of its occurrence. Thus, not the area location, but the presence in a specific building
type with a certain probability of collapsing during an earthquake determines the individual earthquake
risk. As no information is given on the specific map location of the building types, the IR of earthquakes is
not plotted on the map.
6. Sensitivity Analysis The quantification of the risk associated with the different hazards present in Garm might lead to a false
impression of certainty about the results. These results rely on assumptions and simplification and cannot
account completely for the complexity of the real world. Since the risk analysis is about future events,
there will always be some degree of uncertainty (Rausand, 2011, p. 497). The question is, if the results are
robust enough to guide decision-making. Therefore, this section of the report is an attempt to document
where in the risk assessment process uncertainty occurs. In order to do this, three categories of
uncertainty are used: model uncertainty, completeness uncertainty and parameter uncertainty, as
presented by Rausand (2011, p. 500ff.) The aim is to help the reader interpret the results and to point out
where additional research might be useful.
First of all, there is some model uncertainty (Rausand, 2011, 500) inherent in the assessment. It was
decided to use separate event trees for the three hazards and treat them as unrelated events, however,
in reality there are linkages between them. Earthquakes often cause mudslides, landslides in turn can block
the river which can lead to floods. Moreover, some factors, for example the occurrence of an event in
summer or winter, could have an impact on the consequences, since, for instance, it seems plausible that
people may be more likely to be outside their houses (and hence not in a collapsed building in case of an
earthquake) during the summer months. Nevertheless, this could not be included in the scenarios due to
a lack of input data and the scope of the project. This could be investigated further, if a new risk assessment
were to be undertaken. On the other hand, it should be kept in mind that if the number of different, more
detailed scenarios is too big, this will not only make the assessment more difficult but also harder to
understand for the decision makers.
Secondly, it is important to remember that there will always be a completeness uncertainty, i.e. it can
never be assured that all possible hazards are included, because it is unknown what is not known. In a
future risk assessment of Garm there could be a brainstorming to identify other possible hazards.
Thirdly, there is uncertainty about the following values for the different parameters used in the
assessment, since all are based on either assumptions or expert estimates.
12
Frequency and magnitudes of hazard occurrences
Location of people in Garm
length of day/night
location of people in different building types
distribution of people within the population areas
total number of inhabitants
Mortality
likelihoods of building collapse
mortality rates for different hazard scenarios
In order to find out how much of an impact the uncertainty about these parameters has on the results, a
one way sensitivity analysis was carried out. Due to the fact that earthquakes cause the biggest number
of expected fatalities by a very large margin, it was decided to focus on the expected number of fatalities
due to earthquakes, as variations in these scenarios would have the biggest impact on the total number
of expected fatalities from all hazards. Table 3 displays the parameters tested and the reasoning behind
choosing the specific parameters as well as the variation used.
Table 3- Sensitivity Analysis Parameter & Variation
Parameter Value used
in risk assessment
Data source Variation for
sensitivity analysis
Reasoning
Frequency of small
earthquakes 0.075 year mean value
of the range provided by
expert
0.05; 0.1 By providing the frequency as an
interval the expert already expressed her uncertainty about the data. Thus, the minimum and maximum values of the provided interval are used to test
the effect of this uncertainty.
Frequency of large
earthquakes 0.0075 year 0.005; 0.01
Length of a day 12 hours
assumption by
assessment team
10 hours; 14 hours
This parameter measures when people are in different building types and
population areas, however, besides this varying naturally, the experts who provided the data did not give any
indication on the length of a day. For this reason the ratio of day and night
was slightly varied.
Number of people in adobe
buildings
1850 day 6010 night
provided by expert
+500 people in adobe -500 people in
reinforced concrete; -500
people in adobe +500 people in
concrete buildings;
People’s behaviour is hard to model and anticipate and the number of people in the building is subject to variability for example due to a celebration, season, etc. It was attempted to show this by moving a number of people between
adobe buildings and reinforced concrete buildings, however, of course one can conceive of endless combinations of
such movements.
13
Mortality rate in collapsed
building
90% 85%; 95% Rescue capacity due to roads blocked by a landslide or a simultaneously occurring
flood.
Probability of adobe buildings collapsing in a
small earthquake
50% 30%; 70%
Not every building even in the same category will have the same likelihood of collapse. Adobe buildings were chosen, because these are one of the most basic
building types, often build by the inhabitants themselves and not
regulated by building codes, thus variability might be quite big depending
on skills of the builder, age, design of the structure etc.
The tornado diagram in figure 10 illustrates the results of the sensitivity analysis. As can be seen in the
diagram, most of the parameters tested only change the expected number of fatalities by around +/- 15
fatalities or 6%. The uncertainty about the frequency of small earthquakes and the likelihood of building
collapse seem to have the biggest impact on the overall result, changing by around 25%. Hence, in a future
assessment uncertainty could be reduced by doing further research on these parameters. This could be
done for example by consulting multiple experts, looking at historical data or asking local stakeholders.
However, as the uncertainty for both these parameters is essentially aleatory, i.e. caused by natural
variation (Rausand, 2011, p. 499), the uncertainty cannot be completely reduced.
Figure 10 – Tornado diagram displaying sensitivity analysis results
Keeping these uncertainties in mind, the results of this risk assessment may serve as a good basis for
making more informed decisions about mitigation. The results may vary slightly, but the varying levels of
threat stemming from the different hazards in terms of expected numbers of fatalities is still clear.
243
244
245
245
283
291
217
215
215
215
177
169
150 170 190 210 230 250 270 290
Mortality rate
Percentage of day time
Number of people in building types
Frequency for large earthquake
P of adobe building collapsing in small earthquake
Frequency for small earthquake
Sensitivity Analysis Results
14
7. Discussion and Conclusion The results of this risk assessment show that the inhabitants of Garm are exposed to rather big risks from
natural hazards, the potentially most catastrophic but least frequent one being earthquakes. It is
noteworthy that these risks are not distributed equally within the city, both in terms of geographic
location, but also in terms of the building type people are staying in. Mudflows and floods only threaten
parts of the city and while earthquakes will hit the entire city, there is also a geographic component to
this. Adobe buildings are most vulnerable to earthquakes and this building type is not distributed equally
within the city. Assuming that the location of people at night indicates where their homes are, it can be
deduced that the vast majority of these adobe structures seem to be located in population areas 3 to 6
and population area 10, where between 70% and 80% of the area population is in adobe buildings at night.
Looking at the population data, these areas also seem to be the city’s main residential areas, as 7250 of
Garm’s 9500 inhabitants spend the night in one of these areas. These parts of the city are also part of the
areas threatened by mudflows (areas 3-5) and floods (areas 6 and 10). During the day, many people are
concentrated in the city centre (area 1 and 2), where mostly reinforced masonry and steel buildings are
found, which are much safer during earthquakes. In addition, these areas are also safe from mudflows and
albeit there is a flood risk in area 1, this does not cause many fatalities. For this reason, the overall risk of
fatalities due to the natural hazards in Garm is higher at night than during the day. For more information
on the population in the different areas, please see the large version of the structural model in appendix
4.
It might be worth mentioning, that there is also probably a socioeconomic component to the level of risk
an individual in Garm is exposed to, since adobe buildings are cheaper to construct, and thus, often
inhabited by the poorer parts of the population. The individual risk map underlines this consideration, as
unpopular areas due to higher IR are clearly recognizable. However, with Tajikistan being the poorest
country in Central Asia, this probably concerns a large part of Garm’s population (in fact 6010 of its 9500
inhabitants spend the night in adobe buildings). Further research to confirm these speculations could help
shape future policy making.
Furthermore, it should be underlined that this risk assessment, only considers fatalities resulting directly
from these three natural hazards. However, any of the three hazards could have further consequences
threatening human lives and health. For example, even if people are not inside their house when it
collapses or gets flooded, they will still be left homeless which could be catastrophic in itself, especially in
the winter. Moreover, food insecurity is a major threat in Tajikistan affecting almost one third of the
population (World Food Programme, 2015). Natural disasters may exacerbate this problem, as access to
food could be hindered, for example by blocked roads or livestock as well as harvest could be destroyed.
In addition to this, there might be other things that human beings value at risk from natural disasters in
Garm. For instance, while a flood will probably not cause many fatalities, it can potentially be very
destructive to the city’s infrastructure and cause high economic losses. This applies to the two other
hazards.
Moreover, the country underwent a civil war from 1992 to 1997, during which the Rasht Valley was the
centre of opposition (Markowitz, 2011, p. 2013). Even after the war, this area continued to be the location
15
of violent clashes between local leaders and government forces, a hazard in itself that was outside the
scope of this assessment (CIA, 2015). It is conceivable that natural disasters may lead to further political
tensions and civil unrest (for more on this refer to Ahlerup 2009).
These additional consequences could be subject of a more thorough risk assessment that looks at a
broader range of values. Ideally, this would include local stakeholders to find out which values are most
important to them and therefore looked at more closely.
The overall conclusion can be drawn that Garm is exposed to various risk. The most severe hazard in terms
of high fatalities are earthquakes, which is also contributing significantly to the overall risk of Garm, as
depicted in the combined FN-curve. Moreover, the construction types of houses and the hazard frequency
are highly influential on the risk assessment, which requires further consideration of these factors.
Although the three natural hazards are possible to be analysed, the additional factors that might change
the outcome of a mitigation process need to be carefully considered.
16
8. Reference List
Ahlerup, P. (2009). Earthquakes and Civil War. Working paper. Department of Economics. University of
Gothenburg. Retrieved October 8, 2015, from website:
https://gupea.ub.gu.se/bitstream/2077/21202/1/gupea_2077_21202_1.pdf
APA. (2015). Learning APA Style. Retrieved October 8, 2015, from website:
http://www.apastyle.org/learn/index.aspx
CCPS. (2000). Guidelines for Chemical Process Quantitative Risk Analysis. New York: Center for Chemical Process Safety, American Institute of Chemical Engineers (Chapter 4-4.3: Risk Measures p.399).
CIA. (2015). The World Factbook. Retrieved October 8, 2015, from website: https://www.cia.gov/library/publications/the-world-factbook/geos/ti.html
Maptd. (2011). Nuclear Power Plant Locations and Global Seismic Activity. Retrieved September 23,
2015, from http://maptd.com/map/earthquake_activity_vs_nuclear_power_plants/
Markowitz, L. P. (2013). State Erosion: Unbootable Resources and Unruly Elites in Central Asia. Cornell
University Press.
Rausand, M. (2011). Uncertainty and Sensitivity Analysis. In M. Rausand (Ed. 1), Risk Assessment Theory, Methods, and Applications (pp. 497- 513). Published by John Wiley & Sons, Inc., Hoboken, New
Jersey.
Tehler, H. (2015, August). A general framework for risk assessment V 1.4. Division of risk management and societal safety - Lund University
United Nations. (2014). Tajikistan: Building a Democracy. [Video file] Retrieved September 23, 2015,
from http://www.youtube.com/watch?v=C1ey-4PO7fE
UPSeis. (n.d.). Earthquakes Magnitude Scale and Classes. Retrieved September 23, 2015, from website:
http://www.geo.mtu.edu/UPSeis/magnitude.html.
World Food Programme. (2015). Tajikistan Overview. Retrieved October 8, 2015, from website:
http://vam.wfp.org/CountryPage_overview.aspx?iso3=TJK
A
Appendixes
Appendix 1 – Excel Sheet for Data and Calculations
Due to technical constraints, the excel sheet is added as a pdf on the following pages
without corresponding page numbers.
Caption:
Day Night Total
Population area 1 1400 100 1500
Population area 2 1200 700 1900 Day 0.5
Population area 3 500 2500 3000 Night 0.5
Population area 4 300 1600 1900
Population area 5 500 1250 1750
Population area 6 700 1000 1700
Population area 7 500 900 1400
Population area 8 500 400 900
Population area 9 500 150 650
Population area 10 400 900 1300
Outside population area 3000 0 3000
Earthquake scenario Small Large
Small 0.05 0.1 50% 100%
Large 0.01 0.01 30% 100% → 90%
10% 50%
5% 25%
Reinforced concrete
Steel
Probability building collapses
If a building collapses, 90% of the people in the building
will die
warning text
Population area 10
Population area 9
Population area 8
Population area 7Population area 1
Population area 2
Population area 3-5
Population area 6
Number of people
Earthquakes
Frequency
headline total result
Adobe
Unreinforced masonry
Building type Population, day Population, night
Adobe
Reinforced concrete
Steel
60%
10%
Unreinforced masonry 20% 50%
Reinforced concrete 60% 20%
50%
0%
Building type Population, day Population, night
Adobe 10% 30%
Building type Population, day Population, night
Adobe
Building Type
Population area
All Data presented in Garm Case
explanatory text input data linked cell
Unreinforced masonry
0%
30%
0%
50%
check cell
10% 10%
Unreinforced masonry 30%
60%
0%
30%
Reinforced concrete 60%
Steel 0%
Steel 0% 0%
Reinforced concrete 10% 0%
80%
Unreinforced masonry 20% 20%
Reinforced concrete 10% 0%
Steel 0% 0%
Building type Population, day
Unreinforced masonry
Adobe 70% 80%
20% 20%
Building type Population, day Population, night
0%
Building type Population, dayPopulation, night
Steel 10% 0%
Adobe 70%
Population areas
People in types of buildings in percentage
50% 100%
Steel 50% 0%
30%
Reinforced concrete 50% 70%
0%
Steel 40%
Probability of it being day/night
Assumption
Building type Population, day Population, night
Adobe 0% 0%
Unreinforced masonry 10%
Population, night
Adobe 0%
Building type Population, day Population, night
Steel 0% 0%
Unreinforced masonry 30% 30%
Reinforced concrete 0% 0%
Adobe 70% 70%
Reinforced concrete
0%
Unreinforced masonry 0%
Flood scenario Min Max → 2%
Small 0.05 0.5
Large 0.01 0.05
Mudflow scenario
Small Max Min Max Min
Large 0.02 0.005 0.05 0.01 → 90%
0.025 0.005 0.05 0.01
0.01 0.001 0.04 0.01
km² km²
km² km²width km (simplified)
Population area 3 - Mudflow area 1 length km (simplified)
km² km² [mudflow width * length (length Ae = length At)]width km (simplified)
length km (simplified) Population area 8
km²
Population area 4 - Mudflow area 2
km² km²width km (simplified)
length km (simplified)
km²
Population area 5 - Mudflow area 2
km²
km² mudflow width * length (length Ae = length At)
km² mudflow width * length (length Ae = length At)
0.0273 0.3506
0.15850.025
1.44 1.23
0.25 0.6341
1.37 0.36 0.78
Total area size
0.1383
0.92
0.25
0.0138 0.0625
0.13 0.23
Population area 6
Total area sizeSize of small flood affected
area
Size of large flood affected
area
0.8 0.02 0.04
Size of small mudflow
threatened area
Size of large mudflow
threatened area
0.68 0.04 0.1
Population area 7 - Mudflow area 3
Size of small mudflow
exposed area
Size of large mudflow
exposed area
0.94
0.78 0.0026 0.04
0.95 0.04 0.14
Total area size Size of small mudflow Size of large mudflow
Population area 10
0.34 0.1 0.18
Size of small mudflow
exposed area
Size of large mudflow
exposed area
Size of small flood affected
area
Size of large flood affected
area
0.5 0.41 0.49
Population area 9
Total area sizeSize of small flood affected
area
Size of large flood affected
area
Size of small mudflow
threatened area
Size of large mudflow
threatened areaTotal area size
Size of small flood affected
area
Size of large flood affected
area
Total area sizeSize of small flood affected
area
Size of large flood affected
area
0.2734 1.4026
0.771.28
Small mudflow (per
year)
Mudflow area 3
Mudflow area 2
Mudflow area 1
0.22
3 meter above normal
Size of small mudflow
exposed area
Size of large mudflow
exposed area
0.47 0.33 0.45
Total area sizeSize of small flood affected
area
Size of large flood affected
area
Total area sizeSize of small mudflow
threatened area
Size of large mudflow
threatened area
Population area 2
Total area size
Estimated flood frequencies per year
Risk scenario
Small flood
Large flood
2.21 0.35 1.08
Large mudflow (per
year)
Population area 1
Total area sizeSize of small flood affected
area
Size of large flood affected
area
0.92 0.0016
Estimated mudflows frequencies per yearMudflows
Width (kilometer)
0.1
0.25
Find markings on electronic map
Sizes of population areas & affected/ threatened areas & exposed areas (Google earth measurements)
All measurements made in Google earth are displayed in the
appendixes of the report
Floods
Water level rise 2% of people within affected area will drown -->
calculate population density1.5 meter above normal
Case text "… destroying everything in its way." -->
assumption: 100% housing collapse & 90% of people in
collapsed houses die
width km (simplified)length km (simplified)
Persons / km²
Day Night
Population area 1 2979 213
Population area 2 1304 761
Population area 3 226 1131
Population area 4 219 1168
Population area 5 526 1316
Population area 6 875 1250
Population area 7 735 1324
Population area 8 1000 800
Population area 9 1471 441
Population area 10 513 1154
Total population density of total areas
population density = persons per square
kilometer (see calculation in case p.5)
km² mudflow width * length (length Ae = length At) 0.0098 0.0660
0.41 0.53
0.0976 0.2642
Size of small mudflow
exposed area
Size of large mudflow
exposed area
Caption:
Min Max Average 0.0375
Small 0.05 0.1 0.0750 0.0375
Large 0.005 0.01 0.0075 0.0038
0.0038
0.0825
Probability
Small 0.9091
Large 0.0909
Day 0.5
Night 0.5
Number of people in types of buildings
Population area 1
Steel 140 0
Population area 7
Reinforced concrete 840 50
Building type Population, day Population, night
Unreinforced masonry 420
Earthquake data & calculations
explanatory text input data linked cell headline warning text check cell total result
Probability of it being day/night
Earthquake scenario
Total frequency of earthquakes per year
Earthquake scenarioProbabilities are calculated from frequencies by dividing the frequency
scenario small (or large or others) by the total frequency of the event
happening. E.g. P small earthquake = F small / F total --> 0,075
/0,0825
S3: Large, day
S4: Large, night
Frequencies F(scenario)Frequency
S1: Small, day
S2: Small, night
Estimated earthquake frequencies per year
Assumption: total F is
based on average
50
Adobe 0 0
Population area 2
90
Steel 120 0
Unreinforced masonry 150 270
Building type Population, day Population, night
Adobe 120 210 Adobe 50
Building type Population, day Population, night
Unreinforced masonry 240 350
Population area 8
Reinforced concrete 720 140
Building type Population, day Population, night
540
Population area 3
Steel 0 0
Reinforced concrete 300
Adobe 350 2000 Adobe 0
Building type Population, day Population, night
0
280Reinforced concrete 50 0
0
Steel
Adobe 0
0 0
Population area 9
Steel 0 0
490 800
% of people in building types given in sheet 'All data'
multiplied by total area population e.g.
D33 = PRODUKT('All data'!B10;'All data'!D26:E26)
Population area 4
Building type Population, day Population, night
Adobe 210 1280
Building type Population, day Population, night
Unreinforced masonry 100 500
Steel 200 0
Reinforced concrete 250
Unreinforced masonry 50 120
Population area 6
Building type Population, day Population, night
Reinforced concrete 70 0
Unreinforced masonry 140 200
Adobe
Earthquake
F=0.0825/year
Small
P=0.909
day
P=0.5S1 = 0.0375/year
S2=0.0375/yearnight
P=0.5
S3=0.0038/year
S4=0.0038/year
Large
P=0.091
day
P=0.5
night
P=0.5
Day Night
1850 6010
1380 2330
2560 1160
710 0
6500 9500
N fatalities
832.50
372.60
230.40
31.95
Total 1467.45
N fatalities
2704.50
629.10
104.40
0.00
Total 3438.00
N fatalities
1665.00
1242.00
1152.00
159.75
Total 4218.75
N fatalities
5409.00
2097.00
522.00
0.00
100% 1850
Unreinforced masonry 2330
2330
Reinforced concrete 1160 50%
N fatalities = population
in collapsed buildings (D * P) *90% 90% of people
in collapsed building die --> see sheet 'All data' O56
580
116
Steel 0 5% 0
Popu. in buildings P (building collapse)
710
16316500
Steel 365%
Earthquake scenarios consequences - N fatalities
6500 4688
Scenario 4: Large earthquake, night
Building type Popu. in buildings P (building collapse) Popu. in collapsed buildings
9500 3820
Scenario 3: Large earthquake, day
Building type Popu. in buildings P (building collapse) Popu. in collapsed buildings
Adobe 1850
30% 699
Reinforced concrete 1160 10%
Steel 0 25% 0
1380
Reinforced concrete 2560 50% 1280
Steel 710 25% 178
Adobe 6010 100% 6010
Unreinforced masonry 1380 100%
Unreinforced masonry 2330 100%
Scenario 2: Small earthquake, night
Building type Popu. in buildings P (building collapse) Popu. in collapsed buildings
Adobe 6010 50% 3005
250 0Steel 0 0
Reinforced concrete 250Reinforced concrete 30 0
Unreinforced masonry 0 0Unreinforced masonry 60 320
0
Steel 0 0
Reinforced concrete 0
630
Unreinforced masonry 120 270
Adobe 280
Population area 10
Building type Population, day Population, night
150
Steel
Unreinforced masonry 100 250
Reinforced concrete 50 0
Population area 5
Building type Population, day Population, night
Adobe 350 1000
Unreinforced masonry
Reinforced concrete
Steel
Total
Total no° of people in building types
Steel 0 0
Adobe
∑ of calculations above
Scenario 1: Small earthquake, day
Building type
Adobe
Popu. in collapsed buildings
925
414
256
50%
30%
10%
1850
1380
2560
Unreinforced masonry
Reinforced concrete
Total 8028.00
S1
S2
S3
S4
Total E(N) 229.8797
E(N)=∑ f scenarios*N scenarios
e.g. PRODUKT(L86;J10)
9500 8920
55.0294
128.925
15.8203
30.1050
Expected Number of fatalities E(N)
Caption:
Min Max Average (Ø) Frequencies f(scenario)
Small 0.05 0.5 0.275 0.1375
Large 0.01 0.05 0.03 0.1375
0.305 0.015
0.015
Probability
Small 0.9016
Large 0.0984
day 0.5
night 0.5
N
N
N
N
N
N
Ø total F/year (E13) * P small or large
(C18,C19) * P day or night (B23,B24)Ø f small or large / Ø F
total per year
Floods
Assumption: total F is
based on average
Population area 6
Population area 8
Large flood, day Large flood, night Small flood, day
Large flood, day Large flood, night Small flood, day
0.7000 1.0000 0.3500
S5: Small, day
S8: Large, night
Small flood, day
5.2941 1.5882 2.9412
Population area 7
5.7391 3.3478 0.0407
Frequency of occurrence
26.8085 1.9149 19.6596
Population area 2
Large flood, day Large flood, night Small flood, day
Large flood, day Large flood, night Small flood, day
Probability of it being day/night
Total frequency of floods per year
Flood scenario
Population area 1
Population affected by flood scenarios (consequences - N fatalities)
S6: Small, night
S7: Large, day
total result
Flood data & calculations
explanatory text input data linked cell headline warning text check cell
Estimated flood frequencies per year
Large flood, day Large flood, night Small flood, day
1.4706 2.6471 0.5882
9.8000 7.8400 8.2000
Small flood, night
1.4043
Small flood, night
0.0237
Small flood, night
0.5000
Small flood, night
1.0588
Small flood, night
6.5600
Small flood, night
0.8824
values of D and Ae (Ae = At) presented in sheet 'All data'
e.g. B32= PRODUKT('All data'!B122;'All data'!F87:G87)*0.02
N fatalities = population
density (D) * exposed area (Ae) *2% (2% of people in
Ae die --> see sheet 'All data' O64)
Small flood, night
Population area 10
Large flood, day Large flood, night Small flood, day
Population area 9
Large flood, day Large flood, night
Flood
F=0.305/year
Small
P=0.902
day
P=0.5S5 = 0.1375/year
S6=0.1375/yearnight
P=0.5
S7=0.015/year
S8=0.015/year
Large
P=0.098
day
P=0.5
night
P=0.5
N
31.8063
10.4892
50.2226
19.2611
∑ of calculations above
Total fatalities scenarios N(scenario)
0.0600
S6 Small flood, night
Expected Number of fatalities E(N)
0.7533
E(N)=∑ f(scenario)*N(scenario)
(f= J11-J14; N= C60-63)
0.2889
4.3734
M= N/ (D*Ae) = 0,02 [provided in case]
0.4103 0.9231 0.0267
S7 Large flood, day
S8 Large flood, night
S5 Small flood, day
Individual risk of death IR
0.0006
IR=∑f(scenario)*M --> because Ae = At
0.0061
1.4423
6.8579
frequency large flood (J13+J14) * 0.02
S7: Large, day
S8: Large, night
S5: Small, day
S6: Small, night
Total E(N)
IR small flood
IR large flood
frequency small flood (J11+J12)* 0.02 + frequency large
flood (J13+J14) * 0.02
Caption:
Max Min Average Max Min Average0.020 0.005 0.013 0.050 0.010 0.030 0.043 (E9 + H9)
0.025 0.005 0.015 0.050 0.010 0.030 0.045 (E10 + H10)
0.010 0.001 0.006 0.040 0.010 0.025 0.031 (E11 + H11)
0.118
Area 1 0.360 0.015
Area 2 0.381 0.015
Area 3 0.258 0.006
Total 1 0.006
0.015
0.015
large small 0.008
Area 1 0.294 0.706 0.008
Area 2 0.333 0.667
Area 3 0.180 0.820 0.013
0.013
0.003
0.003
Day 0.5
Night 0.5
N
N
N
0.0835 Ae/ At M f(scenario) IR
0.4176 0.3246 0.03 0.0088
0.4462 0.0780 0.013 0.0096
2.2312
0.1432 0.2032 0.0300 0.00549
0.5675 0.0694 0.015 0.0064
0.4689
1.8363 0.4714 0.0300 0.0127
0.1144 0.2450 0.015 0.0160
0.2059
0.1137 0.2717 0.025 0.0061
0.2047 0.1062 0.006 0.0066
6.8333
S20: Large mudflow, night
5.5677 27.8387 71.3992 356.9959
Total E(N)
Expected Number of fatalities E(N)
S9: Small mudflow, day
S10: Small mudflow, night
S11: Large mudflow, day
S16: Large mudflow, night
S17: Small mudflow, day
S18: Small mudflow, night
S19: Large mudflow, day
O 17-30 --> sum of day and night Individual risk of death IR
Assumption: total F is
based on average
Problem with cell connection, so not
directly connected to 'All data'
IR large mudflow
IR small mudflow
Mudflow area 2 - Population area 5
Mudflow area 3 - Population area 7
IR areas = ∑f(scenario) * M * (Ae / At)
90%
Mudflow area 1 - Population area 3
IR large mudflow
IR small mudflow
Mudflow area 2 - Population area 4
IR large mudflow
IR small mudflow
IR large mudflow
IR small mudflow
IR small mudflows = accumulated large & small
mudflow e.g.R57
= PRODUKT(O57;P56;Q57)+R56
M = 90% --> see explanation 'All data'!Q74
E(N)=∑ f(scenario)*N(scenario)
(f= O17-30; N= BEHK 42, 46, 50)
37.8306 62.5255 244.8396
Mudflow area 3 - Population area 7S17: Small mudflow, day S18: Small mudflow, night S19: Large mudflow, day S20: Large mudflow, night
9.1521 16.4737 41.3603 74.4485
S12: Large mudflow, night
S13: Small mudflow, day
S14: Small mudflow, night
S15: Large mudflow, day
N fatalities = Ae * D * M
e.g. S1 = PRODUKT('All data'!D99:E99;'All
data'!B124;'All data'!Q74)
Mudflow area 2 - Population area 4 & 5S13 : Small mudflow, day S14: Small mudflow, night S15: Large mudflow, day S16: Large mudflow, night
9.5483
Mudflow area 1 - Population area 3S10: Small mudflow, nightS9: Small mudflow, day S11: Large mudflow, day S12: Large mudflow, night
F(scenario) =
total F mudflows * P mudflow area * P mudflow size * P time
e.g. F13*B17*C25*B31
Estimated mudflows frequencies per year
Population affected by mudflow scenarios (consequences - N fatalities)
Average frequency small or large / total
frequency areas
(E9-11; H9-11 / J9-11)
Frequency of occurrence Total frequency of mudflows per area
(J9 / F13)
(J10 / F13)
(J11 / F13)
Probability of mudflow being big or small
Probability of it being day/night
Frequencies f(scenario)
S9: Area 1, small, day
S10: Area 1, small, night
S11: Area 1, large, day
S12: Area 1, large, night
S18: Area 3, small, night
S19: Area 3, large, day
Large mudflow (per Small mudflow
S20: Area 3, large, night
S13: Area 2, small, day
S14: Area 2, small, night
S15: Area 2, large, day
S16: Area 2, large, night
S17: Area 3, small, day
Mudflow data & calculations
explanatory text input data linked cell headline warning text check cell
Mudflow area 1
Mudflow area 2
Mudflow area 3
total result
Probability of mudflow in an area given a mudflow occurred
Total frequency of mudflows in all areas per year Ø F =
Mudflow
F=0.118/year
Area 1
P=0.36
small
P=0.71
day
P=0.5S9 = 0.015/year
night
P=0.5S10=0.015/year
large
P=0.29
day
P=0.5S11=0.00625/year
night
P=0.5S12=0.00625/year
Area 2
P=0.38
small
P=0.67
day
P=0.5S13=0.015/year
night
P=0.5S14=0.015/year
large
P=0.33
day
P=0.5S15=0.0075/year
night
P=0.5S16=0.0075/year
Area 3
P=0.26
small
P=0.82
day
P=0.5S17=0.0125/year
night
P=0.5S18=0.0125/year
large
P=0.18
day
P=0.5S19=0.00275/year
night
P=0.5S20=0.00275/year
Caption:
Risk
ScenarioDescription Frequency
Cumulative
FrequencyNo. fatalities Fatalities
S4Earthquake
(large, night) 0.00375 0.00375 8028.00 8028
S3Earthquake
(large, day) 0.00375 0.0075 4218.75 8028
S2Earthquake
(small, night) 0.0375 0.045 3438.00 4218.75
S1Earthquake
(small, day) 0.0375 0.0825 1467.45 4218.75
3438
3438
1467.45
1467.45
100
Frequencies and fatalities
connected to sheet
'Earthquakes'
J10-J13 & L86-L110
Cumulative frequencies
examples: D10 = D9 + C10 and
D11 = D10 + C11
0.0076
0.045
0.045
0.0825
0.0826
Cumulative
Frequency
0
0.00375
0.0038
0.0075
Earthquake scenarios - cumulative frequencies For FN-curve
FN-Curve Earthquakes
explanatory text input data linked cell headline warning text check cell total result
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
100 1000 10000
Fre
qu
en
cy
No of Fatalities
FN-Curve Earthquakes
Caption:
Risk
ScenarioDescription Frequency
Cumulative
FrequencyNo. fatalities Fatalities
S7Flood
(large, day) 0.015 0.015 50.2226 50.2226
S5Flood
(small, day) 0.1375 0.1525 31.8063 50
S8Flood
(large, night) 0.015 0.1675 19.2611 32
S6Flood
(small, night) 0.1375 0.305 10.4892 31.80635
19.26109
19
10.48917
10
1
Frequencies and fatalities
connected to sheet
'Floods'
J11-J14 & L86-L110
Cumulative frequencies
examples: D10 = D9 + C10
and D11 = D10 + C11
0.1525
0.1675
0.1675
0.305
0.305
Cumulative
Frequency
0
0.015
0.015
0.1525
Flood scenarios - cumulative frequencies For FN-curve
FN-Curve Floods
explanatory text input data linked cell headline warning text check cell total result
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 10 100
Fre
qu
en
cy
No of Fatalities
FN Curve Floods
Caption:
Fatalities
Risk
Scenario
Description Frequency Cumulative
Frequency
No.
fatalities 396.6622
S12 Mudflow area 1
(large, night) 0.006 0.006 396.6622 396.6622
S16 Mudflow area 2
(large, night) 0.008 0.014 272.044 272.044
S20 Mudflow area 3
(large, night) 0.003 0.017 82.7206 272.044
S11 Mudflow area 1
(large, day) 0.006 0.023 79.3324 82.7206
S15 Mudflow area 2
(large, day) 0.008 0.031 69.4727 82.7206
S19 Mudflow area 3
(large, day) 0.003 0.034 45.9559 79.3324
S14 Mudflow area 2
(small, night) 0.015 0.049 42.0341 79.3324
S10 Mudflow area 1
(small, night) 0.015 0.064 30.9318 69.4727
S18 Mudflow area 3
(small, night) 0.013 0.077 18.3041 69.4727
S13 Mudlflow area 2
(small, day) 0.015 0.092 10.6092 45.9559
S17 Mudlflow area 3
(small, day) 0.013 0.105 10.169 45.9559
S9 Mudlflow area 1
(small, day) 0.015 0.12 6.1864 42.0341
42.0341
30.9318
30.9318
18.3041
18.3041
10.6092
10.6092
10.169
10.169
6.1864
6.1864
1
Mudflow scenarios - cumulative frequencies
FN-Curve Mudflows
explanatory text input data linked cell headline warning text check cell total result
For FN-curve
0.031
Cumulative
Frequency
0
0.006
0.006
0.014
0.014
0.017
0.017
0.023
0.023
0.031
0.034
0.034
0.049
0.049
0.064
Problem with cell connection,
so not directly connected to
'All data'
0.12
0.12
Cumulative frequencies
examples: D10 = D9 + C10
and D11 = D10 + C11
0.077
0.077
0.092
0.092
0.105
0.105
0.064
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
1 10 100 1000
Fre
qu
en
cy
No of Fatalities
FN Curve Mudflows
Caption:
Risk
ScenarioFrequency No. fatalities Description
Cumulative
FrequencyNo° Frequency
S4 0.0038 8028 Earthquake (large, night) 0.0038 8028 0.0001
S3 0.0038 4218.75 Earthquake (large, day) 0.0076 8028 0.0038
S2 0.0375 3438 Earthquake (small, night) 0.0451 4218.75 0.0038
S1 0.0375 1467.45 Earthquake (small, day) 0.0826 4218.75 0.0076
S12 0.006 396.6622 Mudflow area 1 (large, night) 0.0886 3438 0.0076
S16 0.008 272.044 Mudflow area 2 (large, night) 0.0966 3438 0.0451
S20 0.003 82.7206 Mudflow area 3 (large, night) 0.0996 1467.45 0.0451
S11 0.006 79.3324 Mudflow area 1 (large, day) 0.1056 1467.45 0.0826
S15 0.008 69.4727 Mudflow area 2 (large, day) 0.1136 396.6622 0.0826
S7 0.015 50 Flood (large, day) 0.1286 396.6622 0.0886
S19 0.003 45.9559 Mudflow area 3 (large, day) 0.1316 272.044 0.0886
S14 0.015 42.0341 Mudflow area 2 (small, night) 0.1466 272.044 0.0966
S5 0.1375 32 Flood (small, day) 0.2841 82.7206 0.0966
S10 0.015 30.9318 Mudflow area 1 (small, night) 0.2991 82.7206 0.0996
S8 0.015 19 Flood (large, night) 0.3141 79.3324 0.0996
S18 0.013 18.3041 Mudflow area 3 (small, night) 0.3271 79.3324 0.1056
S13 0.015 10.6092 Mudlflow area 2 (small, day) 0.3421 69.4727 0.1056
S17 0.013 10.169 Mudlflow area 3 (small, day) 0.3551 69.4727 0.1136
S6 0.1375 10 Flood (small, night) 0.4926 50 0.1136
S9 0.015 6.1864 Mudlflow area 1 (small, day) 0.5076 50 0.1286
45.9559 0.1286
45.9559 0.1316
42.0341 0.1316
42.0341 0.1466
32 0.1466
32 0.2841
30.9318 0.2841
30.9318 0.2991
19 0.2991
19 0.3141
18.3041 0.3141
18.3041 0.3271
10.6092 0.3271
10.6092 0.3421
10.169 0.3421
10.169 0.3551
10 0.3551
10 0.4926
6.1864 0.4926
6.1864 0.5076
1 0.5076
Problem with cell
connection, so not directly
connected to 'All data'
All scenarios - cumulative frequencies For FN-curve
FN-Curve Mudflows
input data linked cell headline warning text check cell total result
0.001
0.101
0.201
0.301
0.401
0.501
0.601
1 10 100 1000 10000
Fre
qu
en
cy
No of Fatalities
FN curve Combined Hazards
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
1 10 100 1000 10000Fr
eq
ue
ncy
No of Fatalities
FN Curves Hazards Garm
Earthquakes
Floods
Mudflows
Caption:
Min Avrg Max
Frequency for small earthquake 0.05 0.075 0.1
E(N) 169 230 291
Frequency for large earthquake 0.005 0.0075 0.01
E(N) 215 230 245
Percentage of day time 0.58 0.5 0.42
E(N) Earthquakes 215 230 244
Number of people in building types500 people
less in adobe
buildings
1850/6010
500 people
more in
adobe
buildings
215 230 245
Mortality rate 0.85 0.9 0.95
217 230 243
P of adobe building collapsing in
small earthquake 0.3 0.5 0.7
E(N) Earthquakes 177 230 283
low base high
Mortality rate 217 230 243
Percentage of day time 215 230 244
Number of people in building types 215 230 245
Frequency for large earthquake 215 230 245
P of adobe building collapsing in
small earthquake 177 230 283Frequency for small earthquake 169 230 291
Problem with cell connection, so not
directly connected to 'All data'
Numbers derived from
redoing our calculations
with different values for
the parameters, varying
them one at a time
Sensitivity Analysis
explanatory text input data linked cell headline warning text check cell total result
Earthquake frequency interval
243
244
245
245
283
291
217
215
215
215
177
169
150 170 190 210 230 250 270 290
Mortality rate
Percentage of day time
Number of people in building types
Frequency for large earthquake
P of adobe building collapsing in small earthquake
Frequency for small earthquake
Sensitivity Analysis Results
B
Appendix 2 – Information on Construction Types in Garm
The following data related to the different construction types and the population in the buildings is
provided by the Garm case and based on expert judgements (Garm Case, n.d.).
Appendix figure 1 - The probability that a building collapses, given a certain earthquake risk scenario
Appendix figure 2 – Population in different construction types
C
Appendix 3 – Hazard Frequencies The following estimated frequencies are the expert judgements on the frequency of occurrence of all
three analysed hazard types. The data is retrieved from the Garm Case (n.d.).
Appendix 4 – Large Version Structural Model The structural model in form of a simplified map of Garm is supposed to simplify the risk assessment.
Population areas as well as hazard affect zones are displayed.
Appendix table 1 – Estimated hazards frequencies
Min Max
Small earthquake 0.05 0.1 Large earthquake 0.005 0.01 Small flood 0.05 0.5 Large flood 0.01 0.05 Mudflow area 1 small flood 0.01 0.05 Mudflow area 1 large flood 0.005 0.02 Mudflow area 2 small flood 0.01 0.05 Mudflow area 2 large flood 0.005 0.025 Mudflow area 3 small flood 0.01 0.04 Mudflow area 3 large flood 0.001 0.01
Appendix figure 3 – Structural Model Map Garm
D
Appendix 5 – Table of Scenario Summaries As explained in the main body, the table in appendix table 2 summarizes the results of the twenty risk
scenarios displayed in the trees above as well as the consequences. All displayed values were calculated
in the scenario tree section and the societal risk chapter. The table shows the different scenarios, the areas
which are affected by it and its estimated frequency of occurrence as well as the estimated number of
fatalities in the case that a scenario occurs.
Appendix table 2 - Risk scenarios with frequencies and consequences
Risk Scenario
Effect zone Frequency No. fatalities Description
S1 Entire city of Garm 0.0375 1467.45 Earthquake (small, day)
S2 0.0375 3438.00 Earthquake (small, night)
S3 0.0038 4218.75 Earthquake (large, day)
S4 0.0038 8028.00 Earthquake (large, night)
S5 Population areas 1,2,6,7,8,9,10
0.1375 32 Flood (small, day)
S6 0.1375 10 Flood (small, night)
S7 0.015 50 Flood (large, day)
S8 0.015 19 Flood (large, night)
S9 Population area 3 0.015 6.1864 Mudflow area 1 (small, day)
S10 0.015 30.9318 Mudflow area 1 (small, night)
S11 0.006 79.3324 Mudflow area 1 (large, day)
S12 0.006 396.6622 Mudflow area 1 (large, night)
S13 Population areas 4 and 5
0.015 10.6092 Mudflow area 2 (small, day)
S14 0.015 42.0341 Mudflow area 2 (small, night)
S15 0.008 69.4727 Mudflow area 2 (large, day)
S16 0.008 272.0440 Mudflow area 2 (large, night)
S17 Population area 7 0.013 10.1690 Mudflow area 3 (small, day)
S18 0.013 18.3041 Mudflow area 3 (small, night)
S19 0.003 45.9559 Mudflow area 3 (large, day)
S20 0.003 82.7206 Mudflow area 3 (large, night)
E
Appendix 6 – Individual Risks plotted on Map of Garm
Appendix figure 4 - IR plotted on Map of Garm
F
Appendix 7 – Geographical Area Measurements in Google Earth Screenshots
Total area measurements (given in properties) Example:
Other areas:
Population area 2 3.87 km 0.92 km²
Population area 3 9.40 km 2.21 km²
Population area 4 6.39 km 1.37 km²
Population area 5 5.12 km 0.95 km²
Population area 6 4.73 km 0.80 km²
Population area 7 4.40 km 0.68 km²
Population area 8 3.60 km 0.50 km²
Population area 9 3.20 km 0.34 km²
Population area 10 04.62 km 0.78 km²
G
Affected areas population area 1 (ruler measurements)
Affected areas population area 2
H
Affected areas population area 3
I
1) Width At
2) Lenght At =
Length Ae
calculated:
At total/ At width
3) Mudflow (Ae) width: 250 or 100m
Ae =
Ae width * Ae
length
J
Affected areas population area 4
K
Affected areas population area 5
L
Affected areas population area 6
M
Affected areas population area 7
N
Affected areas population area 8
O
Affected areas population area 9
P
Affected areas population area 10